@inproceedings{fde2e32236dc4d039b081769b18ed957,
title = "SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation",
abstract = "Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performance of standard UNet, few have conducted in-depth analyses of the underlying interest pattern of UNet in medical image segmentation. In this paper, we explore the patterns learned in a UNet and observe two important factors that potentially affect its performance: (i) irrelative feature learned caused by asymmetric supervision; (ii) feature redundancy in the feature map. To this end, we propose to balance the supervision between encoder and decoder and reduce the redundant information in the UNet. Specifically, we use the feature map that contains the most semantic information (i.e., the last layer of the decoder) to provide additional supervision to other blocks to provide additional supervision and reduce feature redundancy by leveraging feature distillation. The proposed method can be easily integrated into existing UNet architecture in a plug-and-play fashion with negligible computational cost. The experimental results suggest that the proposed method consistently improves the performance of standard UNets on four medical image segmentation datasets. The code is available at https://github.com/ChongQingNoSubway/SelfReg-UNet.",
keywords = "Image Segmentation, Interpretability analysis, UNet",
author = "Wenhui Zhu and Xiwen Chen and Peijie Qiu and Mohammad Farazi and Aristeidis Sotiras and Abolfazl Razi and Yalin Wang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 ; Conference date: 06-10-2024 Through 10-10-2024",
year = "2024",
doi = "10.1007/978-3-031-72111-3_56",
language = "English",
isbn = "9783031721106",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "601--611",
editor = "Linguraru, {Marius George} and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Schnabel, {Julia A.}",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings",
}